Image classification in the frequency domain with neural networks and absolute value DCT

Abstract

In this work we explain, how to classify images with neural networks purely in the frequency domain. This is successful by the help of the discrete cosine transform (DCT) in which the values are turned to absolute values. After explaining the method and network architecture we test with a standard dataset for hand written digit recognition and reach the accuracy of 0.9805 in the frequency domain. By superposition of the DCTs we reveal the patterns which are learned by the Network. Afterwards we show some experiments with real images, where the classification in the frequency domain excels the results reached with the same network configuration in the spatial domain.